scAgeCom

scAgeCom maps age-related alterations in intercellular communication across mouse and human tissues by analyzing single-cell RNA sequencing (scRNA-seq) data to build an atlas of aging-associated cell–cell interactions.

Key Features:

• Differential Intercellular Communication Analysis: The scDiffCom R package performs differential intercellular communication analysis between two specified conditions (e.g., young vs aged) in mouse or human scRNA-seq datasets, identifying differential interactions between cell types.
• Curated Ligand–Receptor Interactions: Uses a curated list of ligand–receptor interactions compiled from seven established studies to support interaction inference.
• Extensive Dataset Integration: Applied to large single-cell transcriptomics datasets, including Tabula Muris Senis and the Calico murine aging cell atlas, covering 23 mouse tissues.
• Cross-species scRNA-seq Support: Supports analysis of both mouse and human single-cell RNA-seq datasets for comparative age-related studies.

Scientific Applications:

• Understanding Aging Mechanisms: Maps changes in cell–cell communication across tissues to inform mechanisms underlying the aging phenotype.
• Identification of Age-Related Changes: Highlights widespread age-associated shifts such as upregulation of immune system activities, inflammation, and lipid metabolism, and downregulation of extracellular matrix organization, growth, development, and angiogenesis.
• Facilitating Targeted Research: Enables identification of specific intercellular communication pathways altered with age to guide targeted experimental investigations into age-related diseases.

Methodology:

scAgeCom analyzes single-cell RNA-seq data using the scDiffCom R package to identify differential ligand–receptor-mediated interactions between cell types across two conditions, integrates a curated ligand–receptor list derived from seven studies, and combines data from multiple datasets including Tabula Muris Senis and Calico.